{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T23:03:20Z","timestamp":1784329400626,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819233939","type":"print"},{"value":"9789819233946","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3394-6_25","type":"book-chapter","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:22:11Z","timestamp":1784326931000},"page":"294-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Eff-Jiu: Optimizing MCTS for Tibetan Jiu Chess with Prior Knowledge and Efficient Search Techniques"],"prefix":"10.1007","author":[{"given":"Feng","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiali","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,18]]},"reference":[{"issue":"2","key":"25_CR1","first-page":"157","volume":"4","author":"S Ma","year":"2017","unstructured":"Ma, S., Shang, T.: Inheritance and Protection of Tibetan Chess Culture. Tib. Stud. 4(2), 157\u2013160 (2017)","journal-title":"Tib. Stud."},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1145\/3548608.3559319","volume-title":"Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics","author":"SY Wang","year":"2022","unstructured":"Wang, S.Y., Wu, Q.F.: Tibetan Jiu chess game algorithm based on expert knowledge. In: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics, pp. 842\u2013848. Association for Computing Machinery, New York, NY, USA (2022)"},{"issue":"4","key":"25_CR3","first-page":"577","volume":"13","author":"XL Li","year":"2018","unstructured":"Li, X.L., Wu, L.C., Li, Y.J.: Tibetan JIU computer game research based on chess form. CAAI Trans. Intell. Syst. 13(4), 577\u2013583 (2018)","journal-title":"CAAI Trans. Intell. Syst."},{"issue":"47","key":"25_CR4","doi-asserted-by":"publisher","first-page":"2206625119","DOI":"10.1073\/pnas.2206625119","volume":"199","author":"T McGrath","year":"2022","unstructured":"McGrath, T., et al.: Acquisition of Chess Knowledge in AlphaZero. Proc. Natl. Acad. Sci. 199(47), 2206625119 (2022)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"7587","key":"25_CR5","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature. 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"issue":"7676","key":"25_CR6","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature. 550(7676), 354\u2013359 (2017)","journal-title":"Nature"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Li, X.L., et al.: Review of research on computer games for Tibetan Jiu chess[C]. In: IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, pp. 97\u201399, Auckland, New Zealand (2016)","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.33"},{"issue":"1","key":"25_CR8","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1504\/IJWMC.2022.125530","volume":"23","author":"YJ Wang","year":"2022","unstructured":"Wang, Y.J., et al.: The application of improved UCT combined with neural network in Tibetan JIU chess. Int. J. Wirel. Mob. Comput. 23(1), 22\u201332 (2022)","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"25_CR9","series-title":"Software, and Applications Conference (COMPSAC)","first-page":"390","volume-title":"2023 IEEE 47th Annual Computers","author":"XL Li","year":"2023","unstructured":"Li, X.L., et al.: A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess. In: 2023 IEEE 47th Annual Computers Software, and Applications Conference (COMPSAC), pp. 390\u2013395. IEEE, Torino, Italy (2023)"},{"key":"25_CR10","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1007\/978-981-95-0011-6_38","volume-title":"Advanced Intelligent Computing Technology and Applications","author":"L Song","year":"2025","unstructured":"Song, L., et al.: Expert knowledge-guided deep reinforcement learning for Jiu Chess: a hybrid intelligence approach. In: Advanced Intelligent Computing Technology and Applications, pp. 456\u2013467. Springer Nature, Singapore (2025)"},{"issue":"3","key":"25_CR11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.3233\/ICG-180058","volume":"40","author":"XL Li","year":"2019","unstructured":"Li, X.L., et al.: Strategic research based on chess shapes for Tibetan JIU computer game. ICGA J. 40(3), 318\u2013328 (2019)","journal-title":"ICGA J."},{"issue":"12","key":"25_CR12","first-page":"119","volume":"35","author":"XC Zhang","year":"2021","unstructured":"Zhang, X.C., et al.: An evaluation method for the computer game agent of the intangible heritage Tibetan Jiu chess item. J. Chongqing Univ. Technol. (Nat. Sci.). 35(12), 119\u2013126 (2021)","journal-title":"J. Chongqing Univ. Technol. (Nat. Sci.)"},{"issue":"12","key":"25_CR13","first-page":"110","volume":"36","author":"XL Li","year":"2022","unstructured":"Li, X.L., et al.: A two-staged computer game algorithm for Tibetan Jiu Chess. J. Chongqing Univ. Technol. Nat. Sci. 36(12), 110\u2013120 (2022)","journal-title":"J. Chongqing Univ. Technol. Nat. Sci."},{"key":"25_CR14","series-title":"Software, and Applications Conference (COMPSAC)","first-page":"731","volume-title":"2024 IEEE 48th Annual Computers","author":"C Su","year":"2024","unstructured":"Su, C., et al.: A nested three-stage game algorithm based on chess shape evaluation for Tibetan Jiu Chess. In: 2024 IEEE 48th Annual Computers Software, and Applications Conference (COMPSAC), pp. 731\u2013736. IEEE, Osaka, Japan (2024)"},{"issue":"6419","key":"25_CR15","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1126\/science.aar6404","volume":"362","author":"D Silver","year":"2018","unstructured":"Silver, D., et al.: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science. 362(6419), 1140\u20131144 (2018)","journal-title":"Science"},{"issue":"7839","key":"25_CR16","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/s41586-020-03051-4","volume":"588","author":"J Schrittwieser","year":"2020","unstructured":"Schrittwieser, J., et al.: Mastering Atari, go, chess and shogi by planning with a learned model. Nature. 588(7839), 604\u2013609 (2020)","journal-title":"Nature"},{"issue":"12","key":"25_CR17","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li, Z., et al.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"25_CR18","first-page":"1","volume":"55","author":"A Ghazvini","year":"2025","unstructured":"Ghazvini, A., Abdullah, S.N.H.S., Ayob, M.: Effect of continuous S-shaped rectified linear function on deep convolutional neural network. Appl. Intell. 55(6), 1\u201324 (2025)","journal-title":"Appl. Intell."},{"key":"25_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2024.104184","volume":"249","author":"SE Ribeiro","year":"2024","unstructured":"Ribeiro, S.E., et al.: Distance-based loss function for deep feature space learning of convolutional neural networks. Comput. Vis. Image Underst. 249, 104184 (2024)","journal-title":"Comput. Vis. Image Underst."},{"issue":"1","key":"25_CR20","first-page":"4708075","volume":"2020","author":"XL Li","year":"2020","unstructured":"Li, X.L., et al.: Hybrid online and offline reinforcement learning for Tibetan Jiu Chess. Complexity. 2020(1), 4708075 (2020)","journal-title":"Complexity"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3394-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:22:13Z","timestamp":1784326933000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3394-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,18]]},"ISBN":["9789819233939","9789819233946"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3394-6_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,18]]},"assertion":[{"value":"18 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}